The results show that the GRU multi-way model outperforms the one-way models for all language pairs on all datasets.

all other parameters for the models were identical -we clip the gradient norm to 1.0 (Pascanu et al., 2013), use a dropout of 0.2 and trained the models with adadelta (Zeiler, 2012).


Multi-way NMT systems in both directions improved translation quality (by 3.09 -5.28 BLEU points for Russian→Estonian and 2.16 -4.31 BLEU points for Estonian→Russian) for all three model architectures (deep GRU, convolutional, and transformer), for which we performed multi-way experiments.

the results showed that the most stable architecture for multiway model training was the deep GRU model architecture.


TermEx is very similar to GlossEx with extra extension of entropy-related Domain Consensus (DC) metric.

the term candidate classification is framed as a N-gram classification task rather than the conventional sequence labelling methods that are commonly seen in previous work (Zhou and Su, 2004;Finkel et al., 2004).


The lack of a standard spelling, which introduces intra-dialect and intra-speaker variability.

it contains High German words with their phonetic transcriptions.


If w is a known GSW word then replace w with w and proceed to (4), if not, go to (3).

linguistically, the variety of High German written and spoken in Switzerland is referred to as Swiss Standard German (see Russ (1994), Chapter 4, p. 76-99) and is almost entirely intelligible to German or Austrian speakers.


Linguistically, the variety of High German written and spoken in Switzerland is referred to as Swiss Standard German (see Russ (1994), Chapter 4, p. 76-99) and is almost entirely intelligible to German or Austrian speakers.

training an NMT is not feasible for GSW/DE, as the size of our resources is several orders of magnitude below NMT requirements.


(/sikkalaana nilaiai uruwahiyulathu/) means 'has created a problematic situation', where the word "சிக்கலான" (/sikkalaana/) has meant to be 'problematic' though it can also take the meaning as 'complex', 'issue', or 'conflict'.

and finally, the sixth chapter presents the conclusion of the research along with future work.


Results show that the use of pseudo in-domain data gave positive results in TM and a less significant improvement for LM.

this out-domain data was collected from some freely available sources (Ramasamy et al., 2012, Goldhahn et al., 2012 as well as by web crawling.


title, description, keywords, publisher, author, license etc.).

a list of terms that describe the domain.


The main reason for the relatively low document-level recall is that many document pairs consisting of very short documents were not identified.

we evaluated these methods in the task of reconstructing the English-Greek parallel collection, i.e.


This decision was made under the assumption that code-switching occurs most frequently in spontaneous speech.

the lack of existing speech corpora for code-switched Egyptian Arabic-English is a bottleneck in the creation of ASR systems for conversational Egyptian Arabic.


The overlap was found to be 44.5% in unigrams, 19.2% in bigrams and 5.3% in trigrams.

(Lyu et al., 2015) collected the SEAME corpus, where Mandarin-English audio recordings were collected from interviews and conversational speech and were manually transcribed.


The sentence-level scores were standardized according to each individual assessor's overall mean and standard deviation score.

according to MLT accuracy, for the teams that submitted both constrained and unconstrained models (those using additional external data for training), unconstrained models show improvement over their constrained counterparts in most cases (see Tables 1 and 5).


For teams that submitted both multimodal and text-only systems, the role of multimodality is not evident as far as MLT Accuracy is concerned: sometimes multimodal systems perform better and sometimes text-only systems perform better.

the assessors gave a sentence-level score between 0 and 100, where 0 indicates that the meaning of the source sentence is not preserved in the system output, and 100 means that the meaning is 'perfectly' preserved.


The overall Meteor score of a system is the mean of the sentence-level scores over the test set.

in the training set lehnen occurs 137 times while the rest of the lexical translations combined occur only 16 times.


It assembles user activity data obtained from translation tasks into several languages using a common set of six short English source texts.

other studies have focused on the time course of the translation process (cf.


this is consistent with previous findings that post-editing is less cognitively effortful than translation from scratch (e.g., Green et al., 2013).


For the BML12 from-scratch translation data, there is a consistent pattern where the PWR values for a segment show a steady downward trend as the pauses become longer.

in order to isolate the effects of short monitoring pauses in the translation process and to understand better their influence on cognitive effort, we examine here segment level pause-word ratios in the BML12 and ENJA15 studies for pauses whose lengths fall into different time ranges, specifically 300-500ms, 500-1000ms, 1000-2000ms, 2000-5000ms, and at least 5000ms.


This was also in line with our desideratum not to include meta-information on the sentences, such as being found in two interlinked articles.

we randomly split the documents of the corpora before parallel sentence pair insertion.


MADARi is a web-based interface that supports joint morphological annotation (tokenization, POS tagging, lemmatization) and spelling correction at any point of the annotation process, which minimizes error propagation.

the total number of words to be annotated is about 212,000 words.


These discrepancies are due to differences between the morphological theories adopted by the UD treebanks developers and those employed by the developers of the morphological analyzers.

for Arabic, we adapted the morphological analyzer used in MADAMIRA (Pasha et al., 2014), which is built on top of the databases of SAMA (Maamouri et al., 2010) to output morphology that adheres to the UD Arabic treebank (Taji et al., 2017).


The other project is the so called Baroque corpus (Kieraś et al., 2017).

for example, the word komisja ('commission') appears in f19-1M in the following spellings: komisja, kommisja, komissja, kommissja, komisya, kommisya, komissya, kommissya, komisyja.


Proceduraly, by presenting one symbol at a time on the input layer, a TSOM is prompted to complete the current input string by anticipating the upcoming BMU to be activated.

infinitive, gerund/present participle and past participle forms were added for English, German, italian and Spanish, whereas 3 singular forms of the simple future were included for Modern Greek.


This reduces processing uncertainty, by constraining the range of possible continuations at the stem-suffix boundary of irregularly inflected forms.

in this paper, we investigated inflectional complexity by controlling a number of interacting factors through language-specific training regimes, on which we ran a psycho-linguistically plausible computer model of inflection learning.


We would like to see how well our methods work on compound words it has not seen before.

word formation via compounding is a very widely observed but quite diverse phenomenon across the world's languages, but the compositional semantics of a compound are often productively correlated between even distant languages.


Furthermore, the existing corpora usually do not distinguish sentence pairs which present full matches (both sentences contain the same information), and those that present only partial matches (the two sentences share the meaning only partially), thus not allowing for building customized automated TS systems which would separately model different simplification transformations.

it sometimes increases the number of partial matches which model deletion (see Tables 3 and 4).


Basically these methods optimize some cross-lingual constraints so that the semantic similarity between words corresponds to the closeness of these representations in a common vector space.

tED is a much smaller multilingual data compared to Europarl and contains other languages than European languages.


The contribution of this work is the introduction of a method and its product corpus, KIT-Multi 2 , consisting multilingual word embeddings of English-German-French.

if they need cross-lingual embeddings for a new language pair, they must apply their inducing method on that new bilingual data.


One way of annotating the English sentences would be through human annotators.

since these corpora are publicly available we also release our annotations for the public.


(2017) detail the specific issues of tokenisation for Picard, as well as the choices made.

then, a tagset has to be created or adapted to the language, which requires linguistic expertise.


Transformation of Alsatian spellings for closed class words into their German equivalent in the texts using a custom correspondence dictionary (e.g., Alsatian nìt corresponds to Standard German nicht).

endowing these languages with electronic resources and tools is a major concern for their dissemination, protection and teaching (including for new speakers).


This paper aims at developing a POS tagger for one of the most widely used dialects, namely Gulf Arabic (GA).

table 5 summarises the results of the best systems among all experiments.


The best performance of Bi-LSTM is 91.2% using CC2W+W representation and meta-types and template features.

this suggests that GA is somewhat close to MSA; a similar conclusion reached in (Samih et al., 2017).


We will address this issueamong others -in an inter-annotator agreement experiment described in the following section.


When annotating a historical language, the annotators lack the intuition of a native speaker.

neut 85  60  Masc  11  24  neut  34  0  total  130  84   Table 3: Gender of lîf 1 and strît 1 one could suppose that such a detailed annotation as above could be more difficult for the annotator than only using the asterisk and thus could lead to more disagreement between annotators.


Section 2 discusses graph based semi supervised learning techniques and previous attempts on Tamil POS tagging.

fastText treats each word as composed of ngrams and the vector word is made of the sum of these vectors.


The results concerning the directness, which are depicted in Figure 9, show that there is a significant difference between men and women for Spanish: the Spanish female participants selected the direct options significantly more often than the Spanish male participants did.

by adapting the system's behaviour to the user, the conversation agent may appear more familiar and trustworthy.


Moreover, Burleson (2003) presents a study of culture and gender differences in close relationships, emotion and interpersonal communication.

we explore not only the influence of the user's culture but also of the gender, the frequency of use of speech based assistants as well as the system's role.


For each of these acts, the template file randomly picks an utterance from a pre-defined set.

in the next section we describe how we made use of the proposed architecture by implementing a framework and components that can capture sensor data in a use case scenario performing multi-party multi-modal spoken interaction between humans and a robot.


First, it proposed a solution to handle data-stream synchronisation.

the modular version presented in (Dias et al., 2014).


Continuous Bag-of-Words (CBOW) Each statement was embedded, and each vector was averaged.

a sequence of one-hot word vectors were used for CBOW, LSTM, and BLSTM.


Our assumption is that more important attributes should be presented earlier in the dialogue, and that a user-system dialogue simulation system design (Shah et al., 2018;Liu et al., 2017;Gašić et al., 2017) would require such information to be available.

we count the number of hotels for which each attribute applies.


For example, we observe that attributes describing the "feel", such as "feels chic" or "feels upscale", are mentioned around 700 times, and that for 80% of those times they appear in the first half of the conversation as opposed to the second half, showing that they are often used as general hotel descriptors before diving into detailed attributes.

in order to provide more information on the importance of particular attributes, we analyze where in the conversation (i.e.


Table 2 shows an example of the annotations for a dialogue from the Switchboard Corpus.

this new type was then considered in each subsequent utterance as a possible attribute type -along with the properties from Schema.org -if an attribute instance was found.


This is the first Vietnamese dialog act corpus.

the inter-annotator agreement score is computed by Fleiss kappa measure (Fleiss and Cohen, 1973).


There were several dialog act corpora available to the research community like as TRAINS (Traum, 1996), VERBMOBIL (Alexandersson et al., 1998), SWBD-DAMSL (Jurafsky et al., 1997), MRDA (Shriberg et al., 2004), AMI (McCowan et al., 2005) and so on.

in the pre-processing stage, we build a Vietnamese dialect dictionary includes 167 distinct Southern Vietnam dialect words, 55 distinct Central Vietnam dialect words and their translation to the "standard" North Vietnam dialect.


performative verbs express specific action belong to a purpose such as: Assertives (i.e.


In this annotation, the main proposition (P1) is often not required for annotation, as it is assumed to be marked by the full stop signalling the end of the sentence.

classification experiments are carried out using the Weka 3.8 Workbench (Hall et al., 2009).


Furthermore, conversations are not simply conducted while sitting around a table, which is the case in most conversation corpora.

are is a demonstrative pronoun, so it can be produced without mentioning the object it refers to when spatial-deictically used.


the web pages cover a variety of domains and the first three sentences are long enough to annotate with discourse relations through crowdsourcing.


We developed a computational thinking coding scheme, as shown in Table 1  Every dialogue turn is labeled with one of the codes in Table 1.

the full model architecture is shown in Figure 1.


Ideally the model should be time-variant and uses time step as variable too, because the time/stage of a dialogue may have an impact on the labels.

for LSTM-CRf, our final model has 1024 LSTM units to process sequences of sentence embeddings, and the hidden layers have 1024 and 512 neurons, respectively.


We detect those by inspecting annotated texts for sources of disagreement between annotators, and redefining the annotation scheme so that these disagreements are minimized.

in order to assess the reproducibility of human annotations, we used Cohen's kappa (Cohen, 1960).


However, not considering fact premises as part of the annotation comes unnatural and is inadequate from a point of view of argumentation theory and of application utility.

approximately half of the words were annotated as belonging to an argument component, as can be seen in Figure 1.


Claims are the central components of an argument and they either support or attack the major claim.

the number of cases where such analysis could be carried out is so small that we require a bigger corpus to obtain more significant figures and draw conclusions upon them.


The vocabulary checklist application consisted of three parts.


Collocations can be characterized by different levels of cohesion, ranging from fully fixed idioms (i.e.

the same search on the CORIS corpus (Rossini Favretti 2000) finds 1771 occurrences for nello stesso tempo and just 1908 for allo stesso tempo, so it is necessary to be careful in drawing conclusions from rough data.


Revita lies at the intersection of two established areas of research: intelligent tutoring systems (ITS) and computer-assisted language learning (CALL)the project seeks intelligent solutions for language learning.

the learner needs to get more correct answers in a shorter period of time to win the competition.


Weights are assigned not only to particular words, but also to their various grammatical categories; thus, if a user, e.g., sometimes makes mistakes in a certain nominal case, the system will provide more exercises for this case.

offering a fixed, limited set of exercises is in conflict with the principles of adaptability of the learning process to the profile of the particular user.


This is clearly not the case (x 2 = 12.4, df = 2, p < 0.006, Yates' corrected and excluding the category 'other').

we know very little about the prosodic realization of nouns and verbs in IDS in German.


Occasionally the spelling errors may overlap with grammatical errors.

these properties have to be taken into account in error-annotation.


The SGATe (SLA in Grammatically Annotated Texts) resource comprises the entire EF-Cambridge Open Language Database (EFCAMDAT)  annotated with 107 pedagogically relevant grammatical structures.

the evaluation of a language learner's capacity when producing texts in a foreign language is not an easy task.


In the absence of well-established written traditions and a single authority, the regional languages of France do not have unanimously recognised and accepted standards (Caubet et al., 2002;Vannin et al., 2012).

associating them to modernity (with innovative cartographic tools to present boundaries between dialectal areas), will hopefully help revitalis e our linguistic heritage, or at least give it recognition as a vehicle for creativity, for lack of being able to counteract the decline in minority language practices.


We randomly split our corpus into 80% training, 10% development, and 10% testing data, based on sentences.

turning to the differences between the random and Crúbadán approaches to query generation, for each number of queries, the Crúbadán approach gives many more documents than the random approach, while the number of tokens is not drastically different between the two approaches.


The consistency ratio of Alsatian, when measured this way, is equivalent to that of standard German, despite the lack of a unified orthographic norm in Alsatian, as shown in   In this section, we use the pronunciation dictionaries to train a G2P system.

when there were doubts about some phonemes, specifically stops and their voice or voiceless quality, the waveforms were analyzed using PRAAT (Boersma and weenink, nd).


• Output Phase Finally, the annotation is saved in a database.

in this context, ChAnot 4 is an intelligent web-based annotation tool focused in Peruvian indigenous and agglutinative languages, which allows text processing through morphological (lemma and morphemes), POS-tag, named entity and syntactic annotation (with an interface integrated with BRAT).


The goal of the DLDP is to help minority language speakers' communities in the acquisition of intellectual and practical skills to create, share, and reuse online digital content in their languages.

there are a lot of necessary online and digital resources missing, and speakers often lack information about those that are available.


Using textual features only, there is no significant difference between the performance of the two classifers.

for motion classification, the SVM classifier achieves accuracy of 92.1% and an f1 score of 0.921, while the MLP classifier obtains accuracy of 93.0% and an f1 score of 0.931.


For the two-step model, we also propose an alternative method for determining motion sentiment that infers polarity labels from the relationship to the Government of the speakers who introduce the motions Additionally, we evaluate the use of n-gram textual features and a range of contextual features extracted from metadata related to the speakers.

considering the large overlap between the two sets of labels, for future work or to create larger datasets, manual annotation of these may not be cost-effective.


2 In this step we perform sentence segmentation, tokenization, lemmatization, morphological analysis, part-of-speech tagging and dependency parsing, following the Universal Dependencies scheme (Nivre et al., 2016).

coNLL-U files When extracting the text from the canonical HTML to pass it to UDPipe, we strip away all mark-up and discard all content marked for removal as described in Section 2.2.


Each review is labeled with a manually assigned score of 1-6, as provided by the rating of the original author.

the pre-processing set-up is slightly complicated by the fact that the Norwegian language has two official written standards -Bokmål (the main variety) and Nynorsk -both of which are represented in the review corpus.


In a companion publication (Rouces et al., forthcoming), we discuss applications of this resource in text mining for digital humanities research.

the intersection of the DA test set and the entries with BWS value is used for applying the rank-based measures.


For each political party, we generated 3 extractive and 3 abstractive summaries, building 36 opinion   In order to improve to annotation process, we established some guidelines in relation to how to build the summary, the aspects coverage and the summary length.

the primary research is centralized in some specific languages or domains (mainly English), due to the lack of summaries corpus to evaluate the process in different ones.


Both steps, transcription and annotation, provide valuable information for an SLR system.

the units of interest in the assessment system in our project (cf.


Our analysis showed that the distinction between permissible variants (Category 2) and incorrect productions (Category 3) of a sign was in some cases especially challenging.

participants were specifically instructed to sign the base forms of the lexical items, not modified versions based on the context evoked in the example sentences.


object, but only a noticeable part of the object, as in the RSL sign CAT.

these parameters were only annotated as being iconic or non-iconic, without further analysis of iconicity.


The video data used for the present analyses come mainly from the online dictionary of the Spread The Sign Project (Domfors and Fredäng, 2008) and also from LSD Visual Sign Language Dictionary (Hong Kong Sign Language: http://www.sign-aip.net/ sign-aip/en/home/index.php), Taiwan Sign Language Online Dictionary (Tsay et al.

the rest of the paper is organized as follows: Section 2 introduces some basic principles of lexicostatistics applied to SL, Section 3 describes the web application we created for phonological annotation of signs, Section 4 describes the comparative approach applied to the data, Section 5 reports the results of a case study on four Asian SLs, and Section 6 provides the analysis of handshapes of the sample of 23 SLs.


List(s) of concepts/meanings which are assumed to be universally instantiated in the world's languages (e.g., blood, many, leaf, etc.)

in previous studies, Annotators' subjective perception could affect data evaluation in two steps of the procedure.


The rest of the paper is organized as follows: Section 2 introduces some basic principles of lexicostatistics applied to SL, Section 3 describes the web application we created for phonological annotation of signs, Section 4 describes the comparative approach applied to the data, Section 5 reports the results of a case study on four Asian SLs, and Section 6 provides the analysis of handshapes of the sample of 23 SLs.

previous research has not attempted a systematic comparison of a large sample of SLs.


Unlike translation of texts for which sentence-by-sentence progression is very likely wrong 5 , a picture-by-picture progression this time is often the result everybody expects.

for comparison, we used some of the LS-COLIN data (nonchronological tasks), which we have full access to.


Contrarily to spoken languages, SLs have no written form which can be read out and captured without a trace of the preparation.

in LS-COLiN, informants were given the assignment and elicitation material in a separate room prior to standing in the studio, and could take the time to prepare their production.


To enable studies of higher-level discourse operations such as rhetoric argument building, semantic sectioning and longterm contextualising, we developed a need for both prepared and long Sign Language monologues.

we argue that note support undermines the production whenever they are visible to the informant while they are performing, hence must be avoided then.


In Language resource and evaluation conference (LREC), 6th workshop on the representation and processing of Sign Language: beyond the manual channel, Reykjavik, Islande.

the second argument "tourism" is a focus on the first argument "power" 2.


We used the default settings of Tobii to identify the fixations and saccades, the details of which are described by Ollson (2007).

the main purpose of the corpus was to allow us to analyze the manual segmentation behavior and to identify modalities and features useful for modeling segmentation.


In this paper, to detect when it is difficult for an interpreter to provide an interpretation, statistical analyses of missing  translations in E-J simultaneous interpretations were described.

the cross-tabulation is shown in table 6.


To confirm that the proportions of missing translations are significantly different between each part-of-speech and the overall average, a chi-squared test was applied to the proportions of missing translations of each part-of-speech and the proportions of other content words.

this also contradicts intuitive expectations because the root word is generally the main word of the sentence.


In addition, the proportion of missing translations of numbers is 29.1%, which is 4% less than the average.

in addition, word translation correspondences were aligned manually for each aligned utterance unit.


Note that the differences in the proportions of the missing translations between adjectives, numbers, and other content words are not significant.

names, which are proper nouns, and numbers are regarded as one of the problem triggers (Gile, 1995) and are likely to overload interpreters and be omitted in interpretations.


Here, the horizontal axis is the speech rate and the vertical axis is the proportion of missing translations.

these analyses, which were based on observation, did not clarify the correlation between the identified factors and the occurrence of omissions.


To better identify the pairs of labels that can be easily discriminated from those which can not, only binary models are trained thus resulting in an emotion confusion matrix.

for such speech intervals, the timbre remains the own speaker's timbre or corresponds to another character.


Apart from their research value, spoken language resources are also indispensable in the development of speech recognition systems and related language technologies.

the latter two levels of annotation are provided automatically using the forcedalignment tools developed by (Koržinek et al., 2017).


These efforts have been dwarfed by the vast amounts of user data that are being gathered by multinational corporate giants for the betterment of their proprietary technologies.

for the purposes of this paper, a frame width of 100ms was used throughout, giving each spectrogram a height of 64 pixels, a width of 100 pixels, and a depth of 256 shades.


The corresponding audio snippets are played when the cursor hovers over a given datapoint, so listeners can hover over different regions and listen to hypothetically similar data in quick succession or simultaneously (Edlund et al., 2010) 7 .

it is less efficient if we know nothing of the data (the distribution in space will be random).


The mean values of F 0 exhibit a statistically significant difference between medium and high cognitive load (p < .001).

if the length of the tolerance exceeded the limits of the original audio segment, as much silence as needed was added to fill the 200 ms at the beginning and/or the end.


During this manual SAD correction and segmentation pass, annotators distinguished three categories: • Non-speech.

videos in the main corpus contain speech in English (including US, UK, and other varieties), Mandarin Chinese, and Arabic.


Data scouting was conducted using a customized user interface developed for VAST, known as VScout.

if the recording contains continuous speech for 15 seconds with no noticeable pauses, annotators created a segment on the speech track whose duration is 15 seconds -even if the number of speakers or the languages spoken changed during that segment.


While 6.3% and 9.3% of all sentences appear more than once in the British and French WaCs, respectively, in brWaC only 1.3% are repeated.

for Brazilian Portuguese, we describe how starting with a 1.6 billion token corpus  and following a strict methodology (Section 3), the resulting corpus is expanded to encompass 2.7 billion tokens.


Non-significant words are removed using Part of speech (POS) filtering and for feature selection, the mutual information method is used.

these features are integrated with word features to improve classification results.


This dataset is licensed under the Attribution-NonCommercial-ShareAlike 3.0 Unported License 6 .

text documents are stored in the individual text files using UtF-8 encoding.


news, Wikipedia, crawled webpages or literature (Baroni andBernardini 2004, Biemann et al.

figure 4 shows two distributions for each corpus of German.


Although the choice of this theshold is quite arbitrary, it leads to a good tradeoff between the corpus size and the simplicity of sentences by selecting typically between 5 and 10 percent sentences (see Table 4 for details).

the corpora are still large enough for statistical analyses.


Both initiatives have in common that rather than building comparable corpora from scratch they try to re-use existing corpora and currently mostly rely upon national and reference corpora.


Ph, shown in green, is a monthly psychological magazine.

korAP already offers some unique features for the analysis of DeReko.


The Icelandic Parsed Historical Corpus (IcePaHC) 5 is a diachronic treebank that contains about one million running words from every century between the 12th and the 21st centuries inclusive (Rögnvaldsson et al.

the downloadable corpus is divided into two parts: IGC1 and IGC2.


The study of (GRABAR et al., 2014) proposes a specific lexicon in order to assess which words are potentially non-understandable and then require further explanations.

finally, the access mode as a "physician" user is an interface that offers in the translation point of view the opportunity for the physician to consult the technical term in his or her mother tongue.


Moreover, despite the substantial amount of health-related information available on Internet, little is known about the quality and accessibility of that information.

this implementation is still being developed and assessed as we have done a distinction between term in popular language, by classifying it as a recurrent word mainly in oral talks between patients (for example, in clinic and hospital waiting rooms), and semi-specialist (or informative) term that has its place in informative articles and which is then extracted from written corpus.


This tool is used in the indexing of articles in biomedical journals of PubMed, by the Higher Institute of Health of Rome.

most of these resources are prominently available for English and the access to terminological resources in languages other than English may not be so simple.


In our work we utilize this approach of making data available based on a standardised data model, i.e.

there are approximately 8.1 million Xhosa speakers 3 , adding up to about 16% of the South African population.


The equivalence of all object properties within the BantuLM vocabulary is created with the owl:equivalentProperty property.

further, it can be seen that this WordNet entry ultimately 16 leads to a sense definition of the lexeme reporter.


'These initiatives create a more mobile work environment and consequently reduce our housing footprint.'

finally, we provide some concluding remarks in sSection 7.


For each morphological code in PSC-M we created a corresponding datatype property, subproperty of hasMorpholog-icalTrait, in order to relate a lexical entry with its morphological variants represented as strings.

in the rush to convert all kinds of lexicons and dictionaries into RDF it is perhaps the case that the technical limitations of this mode of publication, as well as the new potentialities which it offers, are not as thoroughly understood as they ought to be.


The MetaNet repository readily provides a substantial number of such metaphors for a non-trivial number of schemas.

it turns out that these three individual instances of linguistic metaphors can be viewed as instantiations of a more general conceptual metaphor HAPPY iS UP.


There is a manual alignment for verbs and nouns, while the validation step was automated.

this study aimed the development of a new lexical and synonym-based resource for the Shipibo-Konibo language.


The greater the result, the relationship between words would be closer.

for instance, for the verb manéxti ("clean" in English), which was taken in the sense whose gloss is "to tie the hairs of the head or crown with other hairs of the same head", the process would be as follows: • Tie the hairs of the head or the crown with other hairs his own head to • Tying, hair, head, crown, hair, head -Only verbs and nouns • Tie, hair, head, crown -Lemmas were extracted and word repetitions were erased The similarity was expressed as a decimal number between -1 and 1 (being the result of a cosine).


Their average precision for the three language pairs are: 91.8% for amod, 90.6% for nmod and 86.2% for vobj.

1 www.dbpedia.org/page/Wiktionary In §3., we present the functions that we have developed in our API to retrieve information from the French Lexical Network in RDF/OWL format.


Lexfom uses the lemon model to represent information about lexical entries and lexical senses.

we had false positives due to parsing errors.


But this definition causes confusions because the modifiers do not always play geo-political entity roles (e.g., "Chinese" in "[GPE Chinese] food").

we also attempted to project English word embeddings to Uyghur using a bilingual lexicon.


We refer the reader to (Poostchi et al., 2016) for more details.

we have released all four word embeddings on GitHub 5 , which will allow easy replication of our experiments.


it is possible that the latter may be indicative of different parameters.


The method showed advantages over the baselines.


NER and NEL have been actively researched topics for decades (Finkel and Manning, 2009;Ratinov and Roth, 2009;Ritter et al., 2011;Derczynski et al., 2015; Nitish Gupta and Roth, 2017).

and MISC, we map the specific gold standard label to these four types for the evaluation.


We exclude single pronouns such as I and me unless they seem extremely contextually relevant, such as in the case of It 7 , when discussing movies with the user.

the resulting entity classification is often coarse and does not encode an ontology.


The lowest layers of the ANN tend to represent task-independent features, whereas the topmost layers are more task-specific.

fewer studies have been performed on transfer learning for ANN-based models in the field of natural language processing.


In the upper part, there are examples of the form "#adv" (meaning either an adverb phrase or an adverb as a word) representing the time expression "when" (i.e.

to the original project of PDT, in these treebanks, the morphological and surface syntactic annotations were done automatically, and the manually annotated deep syntactic layer does not contain annotation of information structure and some other special annotations.


Available treebanks are still, however, largely based on written textual resources, with a few exceptions (Dobrovoljc and Nivre, 2016;Östling et al., 2017).

a list of standardized interjections The extended annotation guidelines for the syntactic annotation of the LIA material is built on the work of Dobrovoljc and Nivre (2016), who describe the annotation of the Slovenian spoken language treebank with Universal Dependencies.


It is to be noted that our benchmarking corpus SandhiKosh is expected to undergo refinements in the future as there is scope for its improvement.

for all the three tools, we considered all the results instead of just the filtered output and marked the output to be correct if any of the results matched with the expected output of Sand-hiKosh.


We introduced the new version 2.0 of the treebank.

cLTT 2.0 contains 483 manually annotated relations classified into 3 categories.


We divided the Chinese Penn Treebank 6 into training, development and test sets as indicated by Tse and Curran (2010).

another mistake observed in parsing subject relative clauses is caused by noun-verb confusion in Chinese.


In (8), the direct object shiqing 'things' is moved to the front of the sentence.

since subject relative clauses and object relative clauses are both very frequent in the data, these two parsing possibilities are very competitive.


After examining the statistics of relative clauses in Stanford dependencies of the training set, we found that when the subject is present in the relative clause while the object is missing, the relative clause is most likely to be an object relative clause; when the subject is missing but the object is present, it is most likely to be a subject relative clause; and if both subject and object are absent, it is most likely to be a subject relative clause.

we labeled an 'of-asso' dependency between the extracted topic and the subject of the relative clause.


After 1200, IO > DO is relatively more frequent, with a peak around 1250.

in summary, we report the development of a shell-based enrichment pipeline for Middle High German including a CoNLL-RDF-based chunker for the analysis of Middle High German syntax and its semantic determinants.


We thus provide a CoNLL-TSV-based enrichment pipeline that includes the chunker in addition to other annotators in order to assess the impact of, for example, semantic factors such as animacy on word order alternation.

while this approach is qualitatively different from conventional parsing, we adopt the terminology of classical Shift-Reduce parsing (Nivre et al., 2007, 100-104): we introduce the properties conll:SHIFT to connect (the root nodes of) adjacent partial parses, and conll:REDUCE to represent attachment within a (partial) parse.


For example, the FTB contains many nominal named entities (tagged N N, e.g for persons), assuming a proper disambiguation step, those could have received a flat:name label instead.

this work was partly funded by the French ANR projects ParSiti (ANR-16-CE33-0021 and SoSweet (ANR-15-CE38-0011-01), as well as by the Program "Investissements d'avenir" ANR-10-LABX-0083 (Labex EFL).


We have seen that the LISCA bins progressively gather occurrences of rarer and less prototypical relation instances, such as left-headed subjects, whose occurrence mainly concentrates in the last four bins.

the list of ordered dependencies was then subdivided into 10 groups, henceforth "bins", each corresponding to 10% of the total.


In Figure 1, it is shown that in all considered languages subjects are mostly right-headed, whereas significant differences are recorded for left-headed subjects whose percentage is much higher for Italian and Spanish (i.e.

we investigated whether algorithms for measuring the plausibility of dependency relations within the output of dependency parsers could be exploited to acquire quantitative evidence from gold treebanks to shed new light in linguistic typological studies.


'Here, from each corner of the world, arrive 300 thousand patients') exemplifies a different, less common, Verb-Subject order involving a nominal subject.

it investigates whether and to what extent algorithms developed for assessing the plausibility of automatically produced syntactic annotations could contribute to shed light on key issues of the linguistic typological literature.


Roller and Erk (2016) demonstrated that the prototypicality learned by CONCAT captures Hearst patterns such as "fruits such as apples."

tween the hypernym frequencies and the mean of the inner products of the trained parameter vector and combined word representations on each hypernym frequency.


2 In addition, we use $subclass for the construction of Adimen-SUMO statements in the few cases where synsets have to relate the knowledge of SUMO at the level of classes.

is not related to any of) the potential SUMO objects that are characterized by the Adimen-SUMO statement proposed above.


From the word similarity point of view, the degree of similarity for 墨鏡"sunglasses" and 專利"patent" should not be high no matter which ontology is applied, for the former is a concrete object but the latter is an abstract one.

that means whenever a event "buy" occurs, that imply the event "pay (money)" happened as well.


For instance, 貓 頭 鷹 'owl' is defined as {bird| 禽 :predication={SelfMove| 自 移 :duration={night| 夜},theme={~}}} and we can find several similar words defineds as the same way.

their distance in an ontology is also far from each other.


This is why we use the principle of Hypernymy linkages for linking such concepts.

some of the synsets in Indian languages are too fine-grained and have a common representation in the English language.


Thereby, they are created by human experts.

india is a vast country with massive language diversity.


Voorhees 1993, 1994, Smeaton, Kelledy and O'Donnell 1995.

ifjeune fille translates as young girl, then PictureQuest will understand that young is an adjective modifying girl.


instead of pressing the corresponding button.

as mentioned earlier, some customer service centers now allow users to say either the option number or a keyword from a list of options/descriptions.


It has to be robust enough to deal with poor recognition quality, inadequate information input by the user, and ambiguous data.

if the speech option for the system is turned on, the speech-based output is generated using Lernout and Hauspie's RealSpeak text-to-speech system.


It should be noted that the set of domain-specific keywords and phrases was provided to the speech recognition system as a text document.

the only known work which automates part of a customer service center using natural language dialogue is the one by Chu-Carroll and Carpenter (1999).


Horiguchi outlined how "spoken language pragmatic information" can be translated (Horiguchi, 1997).

our method achieved a recall of 65% and a precision of 86%.


TDMT uses bottom-up left-to-right chart parsing with transfer rules as shown in Figure 1.

japanese speakers do not have to use polite expression in all utterances.


It is interesting to note that there is no correspondence between the task being performed during the interaction and the amount of changes made to the dialogue.

for instance asking again if it is on Sunday as in $9 in figure 1.


Empirical results obtained from the applications listed in Section 6 have shown that the approach used in the framework is flexible enough and easily portable to new domains, new languages, and new applications.

our approach also has some disadvantages compared with the systems mentioned above.


The framework uses the same engine for all the transformations at all levels because all the syntactic and conceptual structures are represented as dependency tree structures.

in practice, the formalism that we are using for expressing the transformations is inadequate for long-range phenomena (inter-sentential or intra-sentential), including syntactic phenomena such as longdistance wh-movement and discourse phenomena such as anaphora and ellipsis.


The first mapping rule corresponds to one of the lexico-structural transformations used to convert the interlingual ConcS of Figure 3 to the corresponding DSyntS.

for large or rapidly changing grammar (such as a transfer grammar in MT that may need to be adjusted when switching from one parser to another), the burden of the developer's task may be quite heavy.


A match to a subtask is represented by adding the prefix for the subtask to the path of the constraint.

in this case, the action is already satisfied, and is not executed.


Only "high-precision" rules are currently applied to selected types of anaphora.

the system does not currently handle ellipsis.


This is unusual in the field of text processing which has generally dealt with well-punctuated text: some of the most commonly used texts in NLP are machine readable versions of highly edited documents such as newspaper articles or novels.

it is not clear that these techniques will be as successful for ASR text.


Solving this problem will likely require comparing a variety of different settings.

most importantly, the referent for "this person" may have been established in question number 1, and the current question containing the presupposition "this person" is question number 52.


In fact, pipelining is exactly a data-based constraint -the second module in a pipeline does not start until the first one produces its output.

in this section, we present our proposal for a general representation scheme capable of covering the above requirements.


In both cases, the final probabilities are renormalized.

the next two columns indicate respectively the prefixes typed by the user and the completions proposed by the system in a word-completion task.


These sentences have been selected randomly among sentences that have not been used for the training.

the translator remains in control of the translation process and the machine must continually adapt its suggestions in response to his or her input.


The present application benefits from the high modularity of the usage of the components.

no adaptation to the application domain has been made.


In cases where the predictions are compatible, e.g.

the average precision and recall data for the ten test sets are given in table 3, together with the base-line case of assuming that we categorize all unknown words as names (the most common category).


The results for this tree can be found in the second line of Table  3.

since our current data does not include case information we do not include these features.


The remaining thirty percent, or 2100 records, was reserved as the test corpus.

this feature will be less useful in a language such as Japanese or Chinese which use ideographic characters.


For words (mostly proper nouns) that do not appear in WordNet, heuristics are used to determine semantic type.

as discussed in Section 6, the heuristics that comprise the semantic type checking filter do not scale to the test corpus and are the primary reason for the larger percentage of errors attributed to the linguistic filters for that corpus.


We evaluate the system on the TREC8 QA development corpus as well as the TREC8 QA test corpus.

scores on the TREC8 test corpus for systems participating in the QA evaluation ranged between 3 and 146 correct.


As described above, our final version of the QA system ranks summary extracts according to both their vector space similarity to the question as well as linguistic evidence that the answer lies within the extract.

our general approach is to define a new scoring measure that operates on the summary extracts and can be used to reorder the extracts based on linguistic knowledge.


Empire identifies base NPs --non-recursive noun phrases --using a very simple algorithm that matches part-of-speech tag sequences based on a learned noun phrase grammar.

we will continue to use querydependent text summarization in the experiments below.


Her goal then is to answer questions by making inferences about actions and actors in the story using world knowledge in the form of scripts, plans, and goals (Schank and Abelson, 1977).

it appears that it is reasonable to rely implicitly on the IR subsystems to enforce the other linguistic relationships specified in the query (e.g.


More detailed discussion of this database can be found in (Rajan et al.

our decision to apply information extraction technology to binding relationships was guided not only by the biological importance of this phenomenon but also by the relatively straightforward syntactic cuing of binding predications in text.


1997, Voorhees andHarman 1998, andMUC-7); however, several recent efforts have been directed at biomolecular data (Blaschke et al.

phosphatidylinositol transfer protein maps to the corresponding Metathesaurus concept with semantic type 'Amino Acid, peptide, or protein, thus causing it to be more specific than a single lipidbinding site.


The segmentation algorithm is effectively implemented by borrowing the CYK parsing method.

first, we extract compound nouns from a large size of corpus, manually divide them into simple nouns and construct the hand built segmentation dictionary with them.


However, it is difficult to look at a large size of corpus and to assign analyses to it, which makes it difficult to estimate the frequency distribution of words.

_The italic characters such as 'n' or 'x' in analysis information (right column) of the table is used to make distinction between noun and suffix.


Such morphological ambiguity is caused by overgeneration of the morphological analyzer since the analyzer uses less detailed rules for robustness of the system.

just a large amount of lexical knowledge does not make good results if it contains incorrect data and also it is not appropriate to use frequencies obtained by automatically tagging large corpus.


Token-sample performance is used to assess the per-token error rate that one would expect in analyzing large amounts of running text.


They do so for several other corpora as well.

we expect even higher results when testing on every 10th sentence instead of a contiguous set of 10%.


We assume that a morphological analysis consists of three processes: tokenization, dictionary lookup, and disambiguation.

the process can be implemented by a finite state machine or a simple pattern matcher.


Disambiguation i s already language independent, since it does not process strings directly and therefore will not be taken up.


The phrasal recognizer currently only considers processing of simple, non-recursive structures (see fig.

4, where SUBCONJ-CL and REL-CL are tags for subclauses) and verb fragments.


company names) may appear in the text either with or without a designator, we use a dynamic lexicon to store recognized named entities without their designators (e.g., "Braun AG" vs. "Braun") in order to identify subsequent occurrences correctly.

many thanks to Thierry Declerck and milena Valkova for their support during the evaluation of the system.


Hence, the complexity of the recognized structure of the sentence is reduced successively.

an exhaustive description of the covered phenomena can be found in (Braun, 1999).


Each token in the input document is assigned a unique "LFEATURE".

applying a deterministic FST depends linearly only on the input size of the text.


The output document from constrained HMM contains MUC-standard NE.tags such as person, location and organization.

the subsequent modules are focused on location, person and organization names.


In both examples the correct alternative is indicated in parentheses.

on the same test corpus, however, Word only reached 15.9% precision.


Of course, this was an uncontrolled experiment, and there is some potential that information learned from searching with the traditional tools (which were apparently used first) might have provided some benefit when using the conceptual indexing technology.

this led to a reformulated request, move to end of file, which successfully retrieved the passage 9o to end of buffer.


If there are no selected items, mailtool sends copies of those items you axe currently...

two informal evaluations have been conducted that :shed some light on the benefits.


Higher interest rates are normally associated with weaker bond markets.

in WordNet there is a hierarchy fiscal policy -IS-Aeconomic policy -IS-A -policy.


Other companies simply have unusual names.

because of the regularity we observed in company name variants and their use across a variety of news sources, we determined that the knowledge engineering task would be quite repetitive and thus could be automated for most companies.


Name lists provide an extremely efficient way of recognising names, as the only processing required is to match the name pattern in the list against the text and no expensive advanced processing such as full text parsing is required.

the second combination strategy removes any names which appear in the lexicon and occur with a corpus frequency below the filtering probability are removed.


The reduction in accuracy when moving from the 250-byte limit to the 50-byte limit is expected, because much higher precision is required; the 50byte limit allows much less extraneous material to be included with the answer.

for these queries the head noun (e.g., company or city) is extracted, and a lexicon mapping nouns to categories is used to identify the category of the query.


In this paper, we consider the problem of how to evaluate the automatic identification of index terms that have been derived without recourse to lexicons or to other kinds of domain-specific information.

at any given data point, a larger value indicates that a larger percentage of that series' data has that particular rating or better.


We presented subjects with an article and a list of terms identified by one of the three methods.

as [Boguraev and Kennedy 1998] observe, the TT technique may not characterize the full content of documents.


The small difference in average ratings for the HS list and the KW list can be explained, at least in part, by two factors: 1) Differences among professionals and students in inter-subject agreement and reliability; 2) A discrepancy in the rating of single word terms across term types.

we made the following decisions: • For TTs, we included all identified terms; • For HSs, we included all terms whose head occurred more than once in the document; .


Effect of training set size We have measured NE performance in the context of speech as a function of training set size and found that the performance increases logarithmically with the amount of training data for 15% WER test data as well as for 0% WER input.

within each of the regions, we use a statistical bigram language model, and emit exactly one word upon entering each state.


While one may argue that grammar and testsuite should be developed in parallel, such that the coding of a new grammar disjunct is accompanied by the addition of suitable test cases, and vice versa, this is seldom the case.

the work reported here is situated in a large cooperative project aiming at the development of largecoverage grammars for three languages.


In addition, it provided a 3.3-fold increase in creating control measures paired t-test t (3) = 8.298, p < 0.002, one-tailed (see Table I).

the results cannot simply be attributed to the misuse of a hierarchical tool.


Rankboost, like other machine learning programs of the boosting family, can handle a very large number of features.

in this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges.


We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan.

instead of carefully choosing a small number of features by hand which may be useful, we generated a very large number of features and let Rank-Boost choose the relevant ones.


As can be seen, except for "Mr. President" and "President Reagan", all of the examples are either not coreferent or are not people at all.

as the models corresponding to equations 2 and 8 do not include any label-label probabilities, this problem does not appear in these models.


For R = f , as we noted earlier, we want to claim that the new name is a member of the same family as that of the earlier name.

the results of our experiments are as follows: Label% Name% Name 92.6 85.1 Coreference 97.0 94.5 As can be seen, information about possible coreference was a decided help in this task, leading to an error reduction of 59% for the number of labels correct and 63% for names correct.


ME requires subjects to assign numbers to a series of linguistic stimuli in a proportional fashion.

c. The unarmed plane flew very fast and very high.


Since there are enough numb e r o f s u c h w ords (for our purpose), our automatic method could not di erentiate them from true systematic polysemy.

the problem is not only that manual inspection of a large, complex lexicon is very timeconsuming, it is also prone to inconsistencies.


9 Actually, cousin is one of the three relations which indicate the grouping of related senses of a word.

it is useful in knowledge-intensive NLP tasks such as discourse analysis, IE and MT.


Most of the improvement comes from detecting entries that have matching glosses.

over 60% of cognates have at least one gloss in common.


Our best guess is that because the edit detector has high precision, and lower recall, many more words are left in the sentence to be parsed.

they categorize the transitions between words into more categories than we do.


Adapted models were trained using MLLR technique (Legetter and Woodland, (1996)) available as part of the Entropic package.

the results still indicate that generating semi-literal transcriptions may help eliminate the undesirable noise and, at the same time, get the benefits of broader coverage that semi-literal transcripts can afford over NON-LITERAL transcriptions.


This is used to narrow down the search space from all the examples to a much smaller set.

some entries are very general, and are used to translate a large number of expressions.


Given some finite lexicon, the probability of each possible outcome for W n can be estimated using that outcome's relative frequency in a sample.

pursuit of methodological principles 1, 2 and 3 has identified a model capable of describing some of the same phenomena that motivate psycholinguistic interest in other theoretical frameworks.


Theories of initial parsing preferences (Fodor and Ferreira, 1998) suggest that the human parser is fundamentally serial: a function from a tree and new word to a new tree.

these complications do not add any further machinery to the parsing algorithm per se beyond the grammar rules and the dot-moving conventions: in particular, there are no heuristic parsing principles or intermediate structures that are later destroyed.


T 0 is the set of all trees 0 that can possibly attach at Node in tree .

we have consistently seen increase in performance when using the Co-Training method over the baseline across several trials.


The words conveyed in Figure 4 are all words from the corpus that have potential relationships between variants of the word "abuse."

mED has been applied to the morphology induction problem by other researchers (such as Yarowsky and Wicentowski, 2000).


IOE1 An E tag is used to mark the last token of a chunk immediately preceding another chunk.

there are a number of other methods to extend SVMs to multiclass classifiers.


Even if we have no room for applying the voting schemes because of some real-world constraints (limited computation and memory capacity), the use of VC bound may allow to obtain the best accuracy.

in the field of natural language processing, SVMs are applied to text categorization and syntactic dependency structure analysis, and are reported to have achieved higher accuracy than previous approaches.


Conventional machine learning techniques, such as Hidden Markov Model (HMM) and Maximum Entropy Model (ME), normally require a careful feature selection in order to achieve high accuracy.

we employ the simple pairwise classifiers because of the following reasons: (1) In general, SVMs require training cost (where C is the size of training data).


The main idea behind reinforcement learning is to explore the space of possible dialogues and select the strategy which optimizes the expected rewards (Mitchell, 1997, ch.

and availability, and the system retrieves potential activities.


Notably, work on morphology (Mooney and Califf, 1995) and parsing (Thompson et al., 1997) has been carried out.

the efficiency of the rules obviously depends on the way the optimal strategy search space has been modeled and other conditions influencing learning.


The training then proceded by decomposing all parse trees into sequences of SHIFT, PROJECT and ATTACH transitions.

the CPPL is an underbound of the PPL in that it would be the PPL from an ideal parser.


$ # " is segmented as " " ("Koizumi Jun'ichiro" -family and first names) as a person name and " ) " ("September") as a date will be extracted by combining word units.

while we must have a fixed feature set among all NE types in Pairwise method, it is possible to select different feature sets and models when applying One-vs-Rest method.


To distinguish these two situations, we analyze the split level of backbone nodes that begin regions with multiple paths.

winners strongly outpaced losers after Greenspan cut interest rates again.


They assume that paths in dependency trees that take similar arguments (leaves) are close in meaning.

we are not aware of another system generating sentence-level paraphrases.


Using the average precision metric, that version of PI-QUANT was among the top 5 best performing systems out of 67 runs submitted by 34 groups.

we perform passage-level combination to make a wider variety of passages available to the answer selection component, as shown in Figure 2.


9 and S start at the bottom left corner of the parse tree.

the phoneme sequence for the name is taken as the output from the highest scoring path corresponding with the spoken part of the waveform.


Results showed 46.3% accuracy on training data but only 7.9% accuracy on OOV recognition test data.

we anticipate that the multi-stage approach can be improved by folding all three stages into a single recognition server, eventually allowing real-time operation.


Questions belonging to a series were "about" the same subject, and this aboutness could be seen in the use of semantically related words.


Here we seek to partially address this problem by looking at some particular aspect of clarification dialogues in the context of open domain question answering.

question Answering Systems aim to determine an answer to a question by searching for a response in a collection of documents (see Voorhees 2002 for an overview of current systems).


But we will now see that even finding the minimum number of states is NP-complete, and inapproximable.

the general formulation of weighted automata (Berstel and Reutenauer, 1988) permits any weight set K, if appropriate operations ⊕ and ⊗ are provided for combining weights from the different arcs of the automaton.


Formally we know that states 1 and 3 are equivalent because F 1 = F 3 , where F q denotes the suffix function of state q-the function defined by the automaton if the start state is taken to be q rather than 0.

so (λ(Fq)\Fq) is a residue of any residue X of Fq, as claimed.


The total time to compute our λ(F q ) values is therefore O(|states| + t • |arcs|), where t is the maximum length of any arc's weight.

an arc from q i to r i with weight k ∈ ∆ * was reweighted asλ(q i )\(k ⊗λ(r i )).


Sentence pairs where one of the sentences exceeded this limit were ignored in training.

one is bootstrap resampling (Efron and Tibshirani, 1993) 8 to determine confidence intervals, another one splitting the test corpus into a certain number of subcorpora (e.g.


For example, "if Part is an entity ¤ £ and the Whole is a whole 2 then it is not a part-whole relation".

for example, in the pattern "¦ § " the noun phrase that contains the part (X) and the prepositional phrase that contains the whole (Y) form a noun phrase (NP).


We had 29 subjects prompted to say certain inputs in 8 dialogues.

in this paper we present an algorithm for measuring the semantic coherence of sets of concepts against such an ontology.


To our knowledge, there exists no similar software performing semantic coherence scoring to be used for com-parison in this evaluation.


Figure 1 shows the word-cluster distribution.

and finally, we intend to incorporate CatVar into new applications such as parallel corpus word alignment.


The total number of links in this cluster is six, two of which are Porter-determinable and only one of which is naturally-determinable.

if we exclude the not-really missing words, the adjusted recall value becomes 87.16%.


We also extended the parsing strategy slightly to handle Chomsky adjunction structures ( ] ).

the probabilities of parser actions are conditioned on this induced history representation, rather than being conditioned on a set of hand-crafted history features chosen a priori.


If the explicit features include the previous decision ¡ and the other history representations include the previous history representation , then (by induction) any information about the derivation history could conceivably be included in .

a neural network is trained simultaneously to estimate the probabilities of parser actions and to induce a finite repre-sentation of the unbounded parse history.


The actual best parse is figure 2(d), with a score of −18.1.

we have described two general ways of constructing admissible A* estimates for PCFG parsing and given several specific estimates.


We use Bayes rule to reformulate the translation probability for translating a foreign sentence into English  Proceedings of HLT-NAACL 2003 During decoding, the foreign input sentence is segmented into a sequence of phrases ¡ ¢ ¤ £ ¥ .

straight-forward syntax-based mappings do not lead to better translations than unmotivated phrase mappings.


For the source phrase sequence  enforces the requirement that words in the translation agree with those in the phrase sequence.

no us : in fl a ti o n _ g a lo p a n te /4 .8 e − 7 ε : x for the sentence "nous avons une inflation galopante".


All questions are attempted by the prover, but if the proof fails the QA system resorts to other answer extraction methods that were part of the system before the prover.

it remains to create axioms for the ALF of the candidate answer and to start the proof.


So far, only constituents with same syntactic type are treated as paraphrases.

we compare two different ways of estimating the predictive power.


Various techniques such as stop-word removal or stemming require language specific knowledge to design adequately.

although this is a very simple approach, it has not yet been systematically investigated in the literature.


Moreover, whether one can use a purely word-level approach is itself a language dependent issue.

we consider language identification, Greek authorship attribution, Greek genre classification, English topic detection, Chinese topic detection and Japanese topic detection.


Using the disambiguator, the string generated from the most probable reduced f -structure produced by the transfer system is chosen.

in the automatic evaluation of f -structure match, three different system variants were compared.


The PCFG is a Markov grammar (Collins, 1997;Charniak, 2000), i.e.

bacchiani and Roark (2003) presented unsupervised MAP adaptation results for n-gram models, which use the same methods outlined above, but rather than using a manually annotated corpus as input to adaptation, instead use an automatically annotated corpus.


Conditional random fields (CRFs) bring together the best of generative and classification models.

for any label y = c c, c(y) = c is the corresponding chunk tag.


We also used a development test set, provided by Michael Collins, derived from WSJ section 21 tagged with the Brill (1995) POS tagger.

sequence analysis tasks in language and biology are often described as mappings from input sequences to sequences of labels encoding the analysis.


The accuracy rate for individual labeling decisions is over-optimistic as an accuracy measure for shallow parsing.

we disable the preconditioner after a certain number of iterations, determined from held-out data.


In this paper we investigate methods for selecting labeled examples produced by two statistical parsers.

as a point of reference, we plot the improvement rate for a fully supervised parser (same as the one in Figure 2).


The other approach, and the focus of this paper, is co-training (Sarkar, 2001), a mostlyunsupervised algorithm that replaces the human by having two (or more) parsers label training examples for each other.

because the labeled sentences selected by S diff-10% contain more mistakes than those selected by S above-90% , S diff-10% requires slightly more corrections than S above-90% for the same level of parsing performance; though both require fewer corrections than the reference case of no selection (Figure 4(b)).


From all combinations of estimates and measures, document retrieval with a maximum window of 16 words and pointwise mutual information performs best on average in the three test sets used.

landauer and Dumais (1997) applied word similarity measures to answer TOEFl (Test Of English as a Foreign language) synonym questions using latent Semantic Analysis.


Under appropriate conditions, we could reconstruct P(a, b) from these quantities using Gibbs sampling, and, in general, that is the best we can do.

finally, the fact that the measure has much more dynamic range has some appeal when reporting tagging results.


In such models, the probability assigned to a tagged sequence of words x = t, w is the product of a sequence of local portions of the graphical model, one from each time slice.

the principal advantage of the dependency network approach is that advantageous bidirectional effects can be obtained without the extremely expensive global training required for CRFs.


The TREC QA track is a comparative evaluation.

there was some disagreement among the judges for half of all responses that were not obviously wrong.


Using general loglinear regression (Poisson model) under the hypothesis that these two variables are independent of each other, our analysis showed that there is a systematic relationship (significance probability is 0.0504) between source of selected terms and answer accuracy.

the two-tailed t test (df =222) produces a p-value of less than .01% for the comparison of the expected and selected proportions of a) human terms and head sorted terms and b) human terms and technical terms.


There were no immediate reports of injury or damage.

the weekly articles review the daily articles and recite important snippets from the daily news.


In addition, specifically tagged sections of the documents can be searched rather than the entire document, thus providing fast and effective retrieval.

the text enclosed between the start and end tags of all occurrences of each element is encoded using a fixed-width feature vector.


In the following, we consider as a "bigram" a language model with a temporal history that includes information from no longer than one previous time-step into the past.

as can be seen, the FLM alone might increase perplexity, but the GPB-FLM decreases it.


In TREC 2002's main QA evaluation there were 67 different systems or variants thereof involved.

strict corresponds to the correctness criterion used by NIsTthe answer must be exact and justified by the referenced document (assessor judgment v s ).


(3-1) From in-domain examples, the most similar examples are retrieved.

we used an EBMT, DP-match Driven transDucer (D 3 , Sumita, 2001) as a test bed.


A major barrier to the rapid and cost-effective development of spoken language processing applications is the need for time-consuming and expensive human transcription and annotation of collected data.

in order to overcome this we introduce as part of the selection criteria a length limit of 50 phones.


These results suggest that we can achieve the same accuracy as random labeling with around 60% of the effort by active selection of examples according to the confidence-based method described in section 3.

there is also a smaller variation in utterance length between actively and randomly selected training examples (more like 110% than the 150% for HMIHY); table 4 shows that defining effort in terms of number of phones still results in appreciable savings for active learning.


This result is not unreasonable, however, because, due to limited time, very little effort was spent tuning the parameters of the model.

while the syntactic pattern based reranker increases performance using human annotations by nearly 2%, the effect when using automatically extracted information is only 0.5%.


Rules combining sentences or groups of sentences (so called macro segments) to non-primitive RSTtrees.

they introduce massive ambiguities into the grammar which causes the number of analyses to grow according to the Catalan numbers (cf.


1) Ambiguity of parsing is resolved by comparing parse trees of input sentences.

they are regarded as bi-directional paraphrasing rules.


We create a word-trie, transform it into a minimal DFA, then identify hubs.

in matching the gold standard words to divisions predicted by either system, we made the following assumptions.


The hope is that the same techniques will work for extracting prefixes.

we close with a discussion of the limitations and our plans for more complete learning of hub-automata.


We now present an algorithm that checks whether an individual link (e i , f j ) causes a cohesion constraint violation when it is added to a partial alignment.

if two phrases are disjoint in the English sentence, the alignment must not map them to overlapping intervals in the French sentence.


The models attracted many researchers because they are considered to be basic frameworks for retrieving or extracting complex information like events.

our model is robust like keyword-based search, and also enables us to specify the structural and linear positions in texts as done by Region Algebra.


Definition 2 (Relevance of the whole query) Let q be a given query, d be a document and q 1 , ..., q m subqueries of q.

the relevance is defined by the number of portions matched with subqueries in a document.


For this task, we assume that that the frame is a latent class variable (whose domain is the set of lexical units) and the frame elements are variables whose domain is the expanded lexicon (FrameNet + WordNet).

frame Semantics suggests that the meanings of lexical items (lexical units (LU)) are best defined with respect to larger conceptual chunks, called frames.


With this model, we are able to estimate the overall joint distribution for each FrameNet frame, given the lexical items in the candidate sentence from the corpus.

we defined the relevance metric for the WordNet nodes to achieve a larger coverage.


We constructed a large, automatically annotated corpus by merging the output of Charniak's statistical parser (Charniak, 2000) with that of the IBM named entity recognition system Nominator (Wacholder et al., 1997).

the initial examination of the data showed that syntactic forms in coreference chains can be effectively modeled by Markov chains.


The first mention of an entity does have a very special status and its appropriate choice makes text more readable.

there are exceptions which are not surprising; for example, a mention with one modifier is usually followed by a mention with one modifier (probability 0.5) accounting for title modifiers such as "Mr." and "Mrs.".


Unlike Ratnaparkhi we do not directly consider any information about preceding words even the previous one [Toutanova 2002].

they are often written in telegraphic style, omitting closed-class words, which leads to a higher percentage of ambiguous items.


Our results differ partly from observations reported on dialogues.

recall that approach 3. cannot discriminate between positions with an increased or reduced FP probability.


We conducted an evaluation to compare the effectiveness of CarmelTC at analyzing student essays in comparison to LSA, Rainbow, and a purely symbolic approach similar to (Furnkranz et al., 1998), which we refer to here as CarmelTCsymb.

our evaluation demonstrates the advantage of combining predictions from symbolic and "bag of words" approaches for content analysis aspects of automatic essay grading.


For more details see (O'Shaughnessy, 2000).

finally, the values of the main peaks of the spectrum of the speech signal were used as the elements of the third stream vector.


In general, the performance of existing speech recognition systems, whose designs are predicated on relatively noise-free conditions, degrades rapidly in the presence of a high level of adverse conditions.

these tests were repeated using the 2stream feature vector, in which we combined the acoustic distinctive cues to the classical MFCCs and their first derivatives to form two streams (MFCCEDE).


The acoustic distinctive cues are calculated starting from the spectral data using linear combinations of the energies taken in various channels.

results showed also that the use of the auditory-based acoustic distinctive cues improves the performance of the recognition process in noisy car environments with respect to the use of only the MFCCs, their first and second derivatives at high SNR values, but not for low SNR values.


The second component (the upper one) consists of "grammar", "readability", and "verbose and conciseness".

at this point, we are able to produce good prediction of several aspects of information quality, including Depth, Objectivity, Multi-view, and Readability.


As the effect of the two actions (1a) and (2a), it is inferred that the specified location (counter in (1a), bag in (2a)) has been "emptied" of the object (fingerprints in (1a), groceries in (2a)).

(Levin and Rappaport Hovav, 1992) defines classes of verbs according to the ability or inability of a verb to occur in pairs of syntactic frames that preserve meaning.


Probabilistic word segmentation can handle this kind of ambiguity successfully.

thai as well as some other Asian languages has no word boundary delimiter.


In this paper, we present a similar system with a much simpler set of model parameters.

we also ran the N2 on the June 2002 DARPA TIDES Large Data evaluation test set.


One score was for the content of the response and the other for its organization, with each score on a scale of 0-10.

systems should be penalized for not retrieving vital concepts, and penalized for retrieving items that are not on the assessor's concept list at all, but should be neither penalized nor rewarded for retrieving a non-vital concept.


We are interested in continuing these evaluations in two ways.

their system takes as input properly formatted documents and uses the University of Pennsylvania's CAMP system to perform withindocument coreference resolution, doing more careful work to find additional mentions of the entity in the document.


Examples might be the person "John F. Kennedy" who became a president, "White House" -the residence of the US presidents, etc.

the first mention in each group is chosen as the representative (only in Model II and III) and an entity having the same writing with the representative is created for each cluster 3 .


If this approach could be applied to gesturespeech alignment, it would be advantageous because the binding probabilities could be combined with the output of probabilistic recognizers to produce a pipeline architecture, similar to that proposed in (Wu et al., 1999).

we define a binding, b ∈ B, as a tuple relating a gesture, g ∈ G, to a corresponding speech reference, r ∈ R. Provided G and R, the set B enumerates all possible bindings between them.


Building a recognizer that could handle such unconstrained gesture would be a substantial undertaking and an important research contribution in its own right.

the precise definition of a recurrence relation for m[i, j] and a proof of correctness will be described in a future publication.


We address these issues in the rest of the paper.

two possible reasons for this are: first, argument extraction requires more non-local information that is available in the pattern-matching based approach while the classification-based approach relies on local information and is more conducive for identifying the simple predicates in MAtCH.


However, scalability of such systems is a bottleneck due to the heavy cost of authoring and maintenance of rule sets and inevitable brittleness due to lack of coverage in the rule sets.

mATCH (multimodal Access To City Help) is a working city guide and navigation system that enables mobile users to access restaurant and subway information for New York City (NYC) (Johnston et al., 2002b;Johnston et al., 2002a).


Our surface patterns operated both at the word and part-of-speech level.

the same situation is true of the other two questions.


Researchers are then in a position to formulate initial theories, validate the consequences of theories on real data, refine theories in light of empirical data, and follow up with revised experimentation in a dialectic process that forms the essence of scientific discovery.

to this end, we divide the available questions from tREC 2002 into two sets of equal size.


In addition to showing a lower correlation (r linear = 0.82), raw scores also clearly posses a nonlinear component, and in fact the quadratic trend is highly significant (t quad = 13.10, p < .001).

often, such evaluations are based on the use of simple sum-scores (i.e., the number of correct answers) and derivatives thereof (e.g., percentages), or on ad-hoc ways to rank or order system responses according to their correctness.


Our acquisition algorithm uses clues such as itemization or listing in HTML documents and statistical measures such as document frequencies and verb-noun co-occurrences.

the score was designed so that words appearing in many downloaded documents are highly ranked, according to Assumption B.


Alternative 2 This method uses the captions of the itemizations, which are likely to contain a hypernym of the items in the itemization.

a set of the hyponym candidates extracted from a single itemization or list is called a hyponym candidate set (HCS).


But the assumption that prosody conveys information about syntactic structure in the same way that punctuation does could be false.

rb This is based on the bin b of the binned NOrM LAST rHYME DUr value, and is only generated that value is greater than -0.061.


In fact, when transcribing speech, commas are often used to denote a pause.

all of these experiments produced results similar to those reported here.


We motivate the use of tree transducers for natural language and address the training problem for probabilistic tree-totree and tree-to-string transducers.

rounds was motivated by natural language.


We demonstrate this point by utilizing content models to select appropriate sentence orderings: we simply use a content model trained on documents from the domain of interest, selecting the ordering among all the presented candidates that the content model assigns the highest probability to.

to address the first question, we compare summaries created by our system against the "lead" baseline, which extracts the first sentences of the original text -despite its simplicity, the results from the annual Document Understanding Conference (DUC) evaluation suggest that most single-document summarization systems cannot beat this baseline.


The majority of proposals are symbolic and therefore limited to a specific domain due to the large effort involved in hand-coding semantic information (see Lauer 1995 for an extensive overview).

the conceptbased model (see (7)) achieved an accuracy of 28% on this test set, whereas its lexicalized version reached an accuracy of 40% (see table 11).


NIST TREC-9 SDR Web Site (2000) states that: The results of the TREC-9 2000 SDR evaluation presented at TREC on November 14, 2000 showed that retrieval performance for sites on their own recognizer transcripts was virtually the same as their performance on the human reference transcripts.

each query q ∈ Q is presented to the system only once, independent of the number of occurences of q in the transcriptions.


In addition it is possible to find large amounts of text with similar content in order to build better language models and enhance retrieval through use of similar documents.

the second method can only generate phone strings that are substrings of the pronunciations of in-vocabulary word strings.


• Does this word/ POS/ GPOS match the word/ POS/ GPOS that is 1/2/3 positions to its right?

like liu et al., we use a decision tree trained with prosodic features and a hidden event language model for the IP detection task.


Third, they annotate semantic relations among factoids, such as generalization and implication.

here we first compare pyramid scores of the original summaries with DUC scores.


First, pyramid scores ignore interdependencies among content units, including ordering.

our impression from consideration of three SCU inventories is that the pattern illustrated here between SCU1 and SCU2 is typical; when two SCUs are semantically related, the one with the lower weight is semantically dependent on the other.


This tier is the first one top down such that the sum of its cardinality and the cardinalities of tiers above it is greater than or equal to X (summary size in SCUs).

scores can be computed using one, two and so on up to five reference summaries.


This could not only make it difficult to see an improvement in the system's output, but also potentially mislead the BLEU-based optimization of the feature weights.

the use of n-best list rescoring limits the possibility of improvements to what is available in the n-best list.


sVMs are extremely slow in training since they need to solve a quadratic programming search.


Two methods are used to model the aspects of coherence handled by the system.

the thesis statement, main ideas, and conclusion statement should all contain text that is strongly related to the essay topic.


We then obtain a grade level prediction for each passage.

most similar to our work are the vocabulary-based measures, such as the Lexile measure (Stenner et al., 1988), the Revised Dale-Chall formula (Chall and Dale, 1995) and the Fry Short Passage measure (Fry, 1990).


TYPES also obtained the best correlation (0.86) for the Reading A-Z documents.

second, because we are interested in the relative likelihoods of grade levels, accurate relative type probabilities are more important than absolute probabilities.


Comparing these results with the results in Tables 1 and 2, we find that while overall the performance of contextual non-combined feature sets shows a small performance increase over most non-contextual combined or non-combined feature sets, there is again a slight decrease in performance across the best results in each table.

adaBoost's best accuracy of 84.75% is achieved on the "alltext+speech+glob-ident" combined feature set.


The experiments were conducted with an English setup, subjects and assistants in the United States of America and with a German setup, subjects and assistants in Germany.

the flip side, i. e., computer-human interaction (CHI), has received very little attention as a research question by itself.


Figure 2 shows the discrepancy between the dialogue efficiency in Phase 1 (HCI) versus Phase 2 2 The shortest dialogues were 3:18 (E) and 3:30 (G) and the longest 12:05 (E) and 10:08 (G).

the usability of such conversational dialogue systems is still unsatisfactory, as shown in usability experiments with real users (Beringer, 2003) that employed the PROMISE evaluation framework described in Beringer et al.


Section 3 describes and analyzes the results of experiments aimed at comparing the accuracy of speech recognition and the quality of language modeling on both native and non-native data.

the grammar used in this experiment (the "native" grammar) was designed based for native speech without adaptation to non-native data.


In this case, since we wrote the grammar manually and incrementally over time, it is not possible to directly "add the nonnative data" to the grammar.


Ideally, by training acoustic models on target non-native speech, one would capture its specific characteristics just as training on native speech does.

in this paradigm, adapting dialogue systems to non-native speakers does not only mean being able to recognize and understand their speech as it is, but also to help them acquire the vocabulary, grammar, and phonetic knowledge necessary to fulfill the task the system was designed for.


After the addition of all the new features, it is the case that removal of no individual feature except predicate degrades the classification performance significantly, as there are some other features that provide complimentary information.

the best system is trained by first filtering the most likely nulls using the best NULL vs NON-NULL classifier trained using all the features whose argument identification F 1 score is marked in bold in table 4, and then training a ONE vs ALL classifier using the data remaining after performing the filtering and using the features that contribute positively to the classification task -ones whose accuracies are marked in bold in table 4.


The node NP that encompasses "about 20 minutes" is a NON-NULL node, since it does correspond to a semantic argument -ARGM-TMP.

far, in all experiments our unseen test data was selected from the same source as the training data.


This papepr proposes a method of collecting written and spoken language corpora from the Web using interpersonal expressions ( Figure 2).

for example, paraphrasing compound nouns or complex syntactic structure is the task to be tackled.


Such an expression is called interpersonal expression.

the first is that current speech synthesis technology is still insufficient and many applications often produce speech with unnatural accents and intonations.


If the form of target is 'adverb£ noun predicate', the frequency is approximated by that of 'noun predicate', which is counted based on the parse result.

we cannot use existing Japanese spoken language corpora, such as (Maekawa et al., 2000;Takezawa et al., 2002), because they are small.


The results were evaluated through three measures: accuracy of the classification (positive or negative), precision of positive paraphrase pairs, and recall of positive paraphrase pairs.

a lot of attention has been given to applications which uses speech synthesis, for example (Fukuhara et al., 2001).


In Section 4, we describe the method of collecting corpora form the Web and report the experimental result.

it is doubtful whether the connotational difference between paraphrases is sufficiently described in such a lexical resource.


An NP governed by an IP is likely to be a subject, while an NP governed by a VP is more likely to be an object.

although the path feature is sparse, its sparsity may not be a major problem in role recognition.


The alignment template system (Och et al., 1999) is similar to the system described in this work.

the translation results for the Xerox and Canadian Hansards task are very promising.


Also, slightly relaxing the monotonicity constraint in a way that still allows an efficient search is of high interest.

• PER (position-independent word error rate): A shortcoming of the WER is that it requires a perfect word order.


For the Verbmobil task, we have multiple references available.

using a bigram language model and assuming Bayes decision rule, Equation (2), we obtain the following search criterion: = argmax For the preceding equation, we assumed the segmentation probability p(S|e I 1 ) to be constant.


The second variant allows reordering according to the so-called IBM constraints (Berger et al., 1996).

even for the Canadian Hansards task the translation of sentences of length 30 takes only about 1.5 seconds.


it has no children) or all of its children are in G (and it is connected to all of them  Figure 6: Two frontier graph fragments and the rules induced from them.

furthermore, we define a derivation string as an ordered sequence of elements, each of which is either a source symbol or a target subtree.


Figure 2 shows three derivations of the target tree from the source string "il ne va pas", which are all consistent with our definitions.

the span of a node n of the alignment graph is the subset of nodes from S that are reachable from n. Note that this definition is similar to, but not quite the same as, the definition of a span given by Fox (2002).


In this paper, we focused on providing a well-founded mathematical theory and efficient, linear algorithms for learning syntactically motivated transformation rules from parallel corpora.

in the second derivation, "pas" is replaced by the English word "he," which makes no sense.


We will refer to the set of nouns that co-occur with a caseframe as the lexical expectations of the caseframe.

3 These caseframes can capture two types of contextual role information: (1) thematic roles corresponding to events (e.g, "<agent> kidnapped" or "kidnapped <patient>"), and (2) predicate-argument relations associated with both verbs and nouns (e.g., "kidnapped for <np>" or "vehicle with <np>").


Initially, we planned to compare the semantic classes of an anaphor and a candidate and infer that they might be coreferent if their semantic classes intersected.

the Recency KS computes the distance between the candidate and the anaphor relative to its scope.


We combined evidence from four contextual role knowledge sources with evidence from seven general knowledge sources using a Dempster-Shafer probabilistic model.

their work did not consider other types of lexical expectations (e.g., PP arguments), semantic expectations, or context comparisons like our caseframe network.


(In a pilot experiment with a variation that does not throw away information, the entropies are closer to the Gaussian.)

in this section, we help explain the excellent performance of Kneser-Ney smoothing, the best performing language model smoothing technique.


Normally, conjugate gradient methods are only used on data that has a continuous first derivative, so the code was modified to prune weights that go exactly to zero.

as we will show, while GIS uses an update rule of the form our modified algorithm uses a rule of the form (3) Note that there are two different styles of model that one can use, especially in the common case that there are two outputs (values for y.)


Note that the simple learning algorithm is an important contribution: the algorithm for a Gaussian prior is quite a bit more complicated, and previous related work with the Laplacian prior (two-sided exponential) has had a difficult time finding learning algorithms; because the Laplacian does not have a continuous first derivative, and because the exponential prior is bounded at 0, standard gradient descent type algorithms may exhibit poor behavior.

it turns out to be an extremely weak one -it is not uncommon for models, especially those that use all or most possible features, to assign near-zero probabilities (or, if λs may be infi-nite, even actual zero probabilities), and to exhibit other symptoms of severe overfitting.


Compared to the normalized cosine performance of 0.2732, the improved performance of the cosine and normalized cosine measures when source-pair specific information is used (0.2532 and 0.2533, respectively; p .005 for both comparisons) indicates that simple threshold normalization by the running mean is not optimal.

the poorer performance of voting compared to the baseline may be due in part to dependencies among the different measures.


To assess whether the observed differences were significant, we compared models at the .005 significance level using a paired two-sided t-test where the data was randomly partitioned into 10 mutually exclusive sets.

the system can "look ahead" N source files from the current source file being processed when deciding whether the current pair is linked.


For example, sources in a language that is translated to English will consistently use the same terminology, resulting in greater similarity between linked documents with the same native language.

in the TDT link detection task, a link detection system is given a sequence of time-ordered sets of stories, where each set is from one news source.


The second technique is based on discriminatively-trained SVM classifiers (Han et al., 2003).

feature function are represented as f (y t−1 , y t , x).


Average F-measure favors labels with small number of words, which complements word accuracy.

three prior distributions that have shape similar to this empirical distribution are the Gaussian prior, exponential prior, and hyperbolic-L 1 prior, each shown in Figure 2.


The three methods below all provide ways to alter this rate by changing the variance of the Gaussian prior dependent on feature counts.

as we can see word accuracy can be misleading since HMM model even has a higher word accuracy than SVM, although it performs much worse than SVM in most individual fields except abstract.


At this operating point, the discriminative tagger achieves an F-score of 96.08 compared to 94.72 for the HMM, a 25% reduction in error.

instead, we simply iterated for 5 epochs in all cases, regardless of the training set size or number of features used.


At 1,000,000 word of training, the final combination continues to exhibit a 25% reduction in error over the baseline system (because of limitations in the experimental framework discussed earlier, active learning can provide no additional gain at this operating point).

we did not implement cross-validation to determine when to stop training.


A set of 16 tags was used to tag 8 name classes (the seven MUC classes plus the additional null class).

we picked up where Spatter left off, with the clustering algorithm of (Brown et al., 1990).


Chelba and Acero (2004)   ercased sentence e, they find the label sequence T that maximizes p(T |e).

kim and Woodland (2004) and Roark et al.


We showed that using treelets and a tree-based ordering model results in significantly better translations than a leading phrase-based system (Pharaoh, Koehn 2004), keeping all other models identical.

alignments are seldom purely one-to-one and monotone in practice; Figure 1b displays common behavior such as one-to-many alignments, inserted words, and non-monotone translation.